Machine Learning Project to build an algorithm which identifies Enron Employees who may have committed fraud based on the public Enron financial and email dataset.
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Updated
Oct 23, 2017 - HTML
Machine Learning Project to build an algorithm which identifies Enron Employees who may have committed fraud based on the public Enron financial and email dataset.
Investigated factors that affect the likelihood of charity donations being made based on real census data. Developed a naive classifier to compare testing results to. Trained and tested several supervised machine learning models on preprocessed census data to predict the likelihood of donations. Selected the best model based on accuracy, a modif…
Data Mining at UCSB with Professor Sang Oh (Fall 2016)
Stanford's Machine Learning course on Coursera
SVM jupyter notebook
SVM_ML
Multinomial classification tasks in Reddit
Classify candidate exoplanets using various machine learning models like Random Forest, KNN, Logistic Regression and SVM
Binary Classification using Machine Learning
Analysis on Breast Cancer data set using Support Vector Machines, Bayesian Logistic Regression and Naive Bayes coded from scratch.
Practical Artificial Intelligence code
Support Vector Machine by using python and jupyter notebookes , its a Face Detection System
A simple web app that helped students visualize the SVM algorithm according to their choice of hyperparameter setting.
Using data from the Human Activity Recognition to predict correct and incorrect position of the Unilateral Dumbbell Bicep Curls.
A model comparison of Support Vector Machines and Random Forest Classifiers to predict the likelihood of an individual receiving the H1N1 vaccine. Utilizes LASSO Regression for feature reduction.
A sentiment analysis using SPAM/HAM Text Classification data using Support Vector Machines. Utilizes different variations of the Synthetic Minority Oversampling Technique (SMOTE-SVM, SMOTE-KNN).
University of Utah IS 6482 - Data Mining - Taken: Spring 2020
Identify the most efficient machine learning model to identify potential donors. Project covers Linear Regression, Perceptron Algorithm, Decision Trees, Naive Bayes, Support Vector Machines and Ensemble Methods.
University of Utah—MKTG 6600: Business Algorithms | Taken: Fall 2020
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